# coding: utf-8
import sys, os
sys.path.append(os.getcwd())  # 为了导入父目录的文件而进行的设定
from common.functions import *
from common.gradient import numerical_gradient


class TwoLayerNet:

    def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
        # 初始化权重
        self.params = {}
        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
        self.params['b1'] = np.zeros(hidden_size)
        self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
        self.params['b2'] = np.zeros(output_size)

    def predict(self, x):
        W1, W2 = self.params['W1'], self.params['W2']
        b1, b2 = self.params['b1'], self.params['b2']
    
        a1 = np.dot(x, W1) + b1
        z1 = sigmoid(a1)
        a2 = np.dot(z1, W2) + b2
        y = softmax(a2)
        
        return y
        
    # x:输入数据, t:监督数据
    def loss(self, x, t):
        y = self.predict(x)
        
        return cross_entropy_error(y, t)
    # x:输入数据, t:监督数据
    def accuracy(self, x, t):
        y = self.predict(x)
        y = np.argmax(y, axis=1)
        t = np.argmax(t, axis=1)
        
        accuracy = np.sum(y == t) / float(x.shape[0])
        return accuracy
        
    # x:输入数据, t:监督数据
    def numerical_gradient(self, x, t):
        loss_W = lambda W: self.loss(x, t)
        
        grads = {}
        grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
        grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
        grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
        grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
        
        return grads
        
    # def gradient(self, x, t):
    #     W1, W2 = self.params['W1'], self.params['W2']
    #     b1, b2 = self.params['b1'], self.params['b2']
    #     grads = {}
        
    #     batch_num = x.shape[0]
        
    #     # forward
    #     a1 = np.dot(x, W1) + b1
    #     z1 = sigmoid(a1)
    #     a2 = np.dot(z1, W2) + b2
    #     y = softmax(a2)
        
    #     # backward
    #     dy = (y - t) / batch_num
    #     grads['W2'] = np.dot(z1.T, dy)
    #     grads['b2'] = np.sum(dy, axis=0)
        
    #     da1 = np.dot(dy, W2.T)
    #     dz1 = sigmoid_grad(a1) * da1
    #     grads['W1'] = np.dot(x.T, dz1)
    #     grads['b1'] = np.sum(dz1, axis=0)

    #     return grads

if __name__ == "__main__":
    net = TwoLayerNet(input_size=784, hidden_size=100, output_size=10)
    print(net.params['W1'].shape) # (784, 100)
    print(net.params['b1'].shape) # (100,)
    print(net.params['W2'].shape) # (100, 10)
    print(net.params['b2'].shape) # (10,)